Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM

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Prof. Shah Saloni Niranjan; Mr. Pawar R.B.; Ms. Raut A.S.; Ms. Jadhav S.N.

Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM

Authors: Prof. Shah Saloni Niranjan, Mr. Pawar R.B., Ms. Raut A.S., Ms. Jadhav S.N.

Unique Paper ID: 180483
Volume: 12
Issue: 1
PageNo: 1337-1341

Keywords: Machine Learning Lung Sound Analysis GTCC Features Deep Learning Respiratory Diseases.

Abstract:
Respiratory diseases rank among the foremost causes of mortality globally. While traditional lung auscultation is effective, it is hindered by limitations such as interference from background noise and reliance on the expertise of healthcare professionals. Recently, machine learning has emerged as a promising approach for the automated analysis of lung sounds, enhancing diagnostic accuracy and reducing the time required for diagnosis. This study is dedicated to the development of an automated system for lung sound classification, utilizing GTCC-based features in conjunction with a Multi-Layer Perceptron (MLP) classifier. Our system, trained on a comprehensive dataset comprising over 6,800 audio clips, achieved an impressive classification accuracy of 99.22%, underscoring its potential to facilitate the early detection of respiratory diseases.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 1
PageNo: 1337-1341

Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM

Available:https://ijirt.org/article?manuscript=180483

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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